Mutual Information-driven Pan-sharpening
Man Zhou, Keyu Yan, Jie Huang, Zihe Yang, Xueyang Fu, Feng Zhao
Abstract
Pan-sharpening aims to integrate the complementary information of texture-rich PAN images and multi-spectral (MS) images to produce the texture-rich MS images. Despite the remarkable progress, existing state-of-the-art Pansharpening methods don't explicitly enforce the complementary information learning between two modalities of PAN and MS images. This leads to information redundancy not being handled well, which further limits the performance of these methods. To address the above issue, we propose a novel mutual information-driven Pan-sharpening framework in this paper. To be specific, we first project the PAN and MS image into modality-aware feature space independently, and then impose the mutual information minimization over them to explicitly encourage the complementary information learning. Such operation is capable of reducing the information redundancy and improving the model performance. Extensive experimental results over multiple satellite datasets demonstrate that the proposed algorithm outperforms other state-of-the-art methods qualitatively and quantitatively with great generalization ability to real-world scenes.